With the proliferation of high-speed internet access and the availability of cheap secondary storage it has become very easy for home users to copy large amounts of data and distribute it over the web. That shared data typically contains much self-produced or free content, but in many cases also copyrighted material from third parties.
Despite the poor image quality, thousands of low-resolution videos are uploaded every day to video-sharing sites such as YouTube. It is known that this material includes a significant percentage of copyrighted movies. Other file sharing platforms, such as eDonkey or BitTorrent, are also very popular, yet illegal, sources of copyrighted material. In 2005, a study conducted by the Motion Picture Association of America was published, which estimated that their members lost 2.3 billion US$ in sales due to video piracy over the Internet (“The Cost of Movie Piracy”). Due to the high risk of piracy, movie producers have tried many means of restricting illegal distribution of their material, albeit with very limited success. Video pirates have found ways to circumvent even the most clever protection mechanisms. In order to cover up their tracks, stolen (ripped) videos are typically compressed, modified and re-encoded, making them more suitable for easy downloading. Another very popular method for stealing videos is “camcording”, where pirates smuggle digital camcorders into a movie theatre and record what is projected on the screen, and upload it to the web.
A fully automatic content-based video identification system builds a reference database of low-level features (called descriptors) extracted from the videos to protect. Then, video streams to check are fed into the system to detect near- or close-duplicate content. Those streams can originate from the web (via a robot), from a TV-broadcast or from a camera installed in front of a multimedia device; a service such as YouTube could even submit uploaded videos to the system.
Detecting low-quality or even severely altered videos in very large reference databases in real-time (preferable several income streams simultaneously when monitoring TV channels) is crucial in order for a system to be usable in practice. Missing a few extremely distorted videos is generally acceptable, while it must avoid false positives at all cost.
Particular video copyright related applications have specific needs, however. Querying the system using small fractions of a video file found on the web might be sufficient to assess whether it is stolen material. Monitoring an incoming video stream to spot trailers or program boundaries requires a more detailed and continuous querying process. Furthermore, video copyright protection focuses for the most part on very recent videos. Popular movies typically make the major part of their profit in the first few months after publication. Nevertheless most movies are available online for download right after the premiere, sometimes even before. Supporting database updates is therefore key issue.
US 2006/083429 and EP 1650683 disclose the DPS2 CBCR video retrieval system using a local descriptor type, which are 20 dimensional on the lower end of the dimensionality scale. The local descriptors used in the DPS2 are based on the Harris interest point detector, and encode both, spatial and temporal information around the selected interest point. Furthermore, there is a step in the DPS2 system where frames of interest are selected, similar to the detection of interest points in the descriptor extraction process. This is achieved by examining the rate of motion in the source video material at a pixel level, selecting frames where overall motion in the frame is either at its least or at its most, compared to the frames around it.
It is an object of the invention to provide a method and a system for an efficient and preferably fast close-duplicate search and identification within large collections of data streams, which overcomes the deficiencies of the prior art. It is a further object of the invention to provide a method and system for close-duplicate identification which is robust towards strong modifications of the content in the stream.
These and other objects are achieved by the features of the independent claims. Further preferred embodiments are characterized in the dependent claims and discussed in the description below.